OpenRaiser/Intern-Atlas huggingface dataset:
A Deep Learning Approach to Detect Lean Blowout in Combustion Systems
Venue: arXiv cs.LG 2022
Year: 2022

Lean combustion is environment friendly with low NOx emissions and also provides better fuel efficiency in a combustion system. However, approaching towards lean combustion can make engines more susceptible to lean blowout. Lean blowout (LBO) is an undesirable phenomenon that can cause sudden flame extinction leading to sudden loss of power. During the design stage, it is quite challenging for the scientists to accurately determine the optimal operating limits to avoid sudden LBO occurrence. Therefore, it is crucial to develop accurate and computationally tractable frameworks for online LBO detection in low NOx emission engines. To the best of our knowledge, for the first time, we propose a deep learning approach to detect lean blowout in combustion systems. In this work, we utilize a laboratory-scale combustor to collect data for different protocols. We start far from LBO for each protocol and gradually move towards the LBO regime, capturing a quasi-static time series dataset at each condition. Using one of the protocols in our dataset as the reference protocol and with conditions annotated by domain experts, we find a transition state metric for our trained deep learning model to detect LBO in the other test protocols. We find that our proposed approach is more accurate and computationally faster than other baseline models to detect the transitions to LBO. Therefore, we recommend this method for real-time performance monitoring in lean combustion engines.
The Role of Diversity in Cybersecurity Risk Analysis: An Experimental Plan
Venue: arXiv cs.SE 2022
Year: 2022

Cybersecurity threat and risk analysis (RA) approaches are used to identify and mitigate security risks early-on in the software development life-cycle. Existing approaches automate only parts of the analysis procedure, leaving key decisions in identification, feasibility and risk analysis, and quality assessment to be determined by expert judgement. Therefore, in practice teams of experts manually analyze the system design by holding brainstorming workshops. Such decisions are made in face of uncertainties, leaving room for biased judgement (e.g., preferential treatment of category of experts). Biased decision making during the analysis may result in unequal contribution of expertise, particularly since some diversity dimensions (i.e., gender) are underrepresented in security teams. Beyond the work of risk perception of non-technical threats, no existing work has empirically studied the role of diversity in the risk analysis of technical artefacts. This paper proposes an experimental plan for identifying the key diversity factors in RA.
Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis
Venue: arXiv cs.LG 2022
Year: 2022

Imitation learning learns a policy from expert trajectories. While the expert data is believed to be crucial for imitation quality, it was found that a kind of imitation learning approach, adversarial imitation learning (AIL), can have exceptional performance. With as little as only one expert trajectory, AIL can match the expert performance even in a long horizon, on tasks such as locomotion control. There are two mysterious points in this phenomenon. First, why can AIL perform well with only a few expert trajectories? Second, why does AIL maintain good performance despite the length of the planning horizon? In this paper, we theoretically explore these two questions. For a total-variation-distance-based AIL (called TV-AIL), our analysis shows a horizon-free imitation gap $\mathcal O(\{\min\{1, \sqrt{|\mathcal S|/N} \})$ on a class of instances abstracted from locomotion control tasks. Here $|\mathcal S|$ is the state space size for a tabular Markov decision process, and $N$ is the number of expert trajectories. We emphasize two important features of our bound. First, this bound is meaningful in both small and large sample regimes. Second, this bound suggests that the imitation gap of TV-AIL is at most 1 regardless of the planning horizon. Therefore, this bound can explain the empirical observation. Technically, we leverage the structure of multi-stage policy optimization in TV-AIL and present a new stage-coupled analysis via dynamic programming
N-RPN: Hard Example Learning for Region Proposal Networks
Venue: arXiv cs.CV 2022
Year: 2022

The region proposal task is to generate a set of candidate regions that contain an object. In this task, it is most important to propose as many candidates of ground-truth as possible in a fixed number of proposals. In a typical image, however, there are too few hard negative examples compared to the vast number of easy negatives, so region proposal networks struggle to train on hard negatives. Because of this problem, networks tend to propose hard negatives as candidates, while failing to propose ground-truth candidates, which leads to poor performance. In this paper, we propose a Negative Region Proposal Network(nRPN) to improve Region Proposal Network(RPN). The nRPN learns from the RPN's false positives and provide hard negative examples to the RPN. Our proposed nRPN leads to a reduction in false positives and better RPN performance. An RPN trained with an nRPN achieves performance improvements on the PASCAL VOC 2007 dataset.
Spectrum Focused Frequency Adversarial Attacks for Automatic Modulation Classification
Venue: arXiv cs.CR 2022
Year: 2022

Artificial intelligence (AI) technology has provided a potential solution for automatic modulation recognition (AMC). Unfortunately, AI-based AMC models are vulnerable to adversarial examples, which seriously threatens the efficient, secure and trusted application of AI in AMC. This issue has attracted the attention of researchers. Various studies on adversarial attacks and defenses evolve in a spiral. However, the existing adversarial attack methods are all designed in the time domain. They introduce more high-frequency components in the frequency domain, due to abrupt updates in the time domain. For this issue, from the perspective of frequency domain, we propose a spectrum focused frequency adversarial attacks (SFFAA) for AMC model, and further draw on the idea of meta-learning, propose a Meta-SFFAA algorithm to improve the transferability in the black-box attacks. Extensive experiments, qualitative and quantitative metrics demonstrate that the proposed algorithm can concentrate the adversarial energy on the spectrum where the signal is located, significantly improve the adversarial attack performance while maintaining the concealment in the frequency domain.
Decay2Distill: Leveraging spatial perturbation and regularization for self-supervised image denoising
Venue: arXiv cs.CV 2022
Year: 2022

Unpaired image denoising has achieved promising development over the last few years. Regardless of the performance, methods tend to heavily rely on underlying noise properties or any assumption which is not always practical. Alternatively, if we can ground the problem from a structural perspective rather than noise statistics, we can achieve a more robust solution. with such motivation, we propose a self-supervised denoising scheme that is unpaired and relies on spatial degradation followed by a regularized refinement. Our method shows considerable improvement over previous methods and exhibited consistent performance over different data domains.
A Brief Note on Building Augmented Reality Models for Scientific Visualization
Venue: arXiv cs.GR 2022
Year: 2022

Augmented reality (AR) has revolutionized the video game industry by providing interactive, three-dimensional visualization. Interestingly, AR technology has only been sparsely used in scientific visualization. This is, at least in part, due to the significant technical challenges previously associated with creating and accessing such models. To ease access to AR for the scientific community, we introduce a novel visualization pipeline with which they can create and render AR models. We demonstrate our pipeline by means of finite element results, but note that our pipeline is generally applicable to data that may be represented through meshed surfaces. Specifically, we use two open-source software packages, ParaView and Blender. The models are then rendered through the <model-viewer> platform, which we access through Android and iOS smartphones. To demonstrate our pipeline, we build AR models from static and time-series results of finite element simulations discretized with continuum, shell, and beam elements. Moreover, we openly provide python scripts to automate this process. Thus, others may use our framework to create and render AR models for their own research and teaching activities.
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess
Venue: arXiv cs.AI 2022
Year: 2022

In this work, we adapt a training approach inspired by the original AlphaGo system to play the imperfect information game of Reconnaissance Blind Chess. Using only the observations instead of a full description of the game state, we first train a supervised agent on publicly available game records. Next, we increase the performance of the agent through self-play with the on-policy reinforcement learning algorithm Proximal Policy Optimization. We do not use any search to avoid problems caused by the partial observability of game states and only use the policy network to generate moves when playing. With this approach, we achieve an ELO of 1330 on the RBC leaderboard, which places our agent at position 27 at the time of this writing. We see that self-play significantly improves performance and that the agent plays acceptably well without search and without making assumptions about the true game state.
SmartControllerJS: A JavaScript library to turn smartphones into controllers for web-based interactive experiments
Venue: arXiv cs.HC 2022
Year: 2022

We introduce SmartControllerJS, a new JavaScript library for fast, cost-effective designing of web applications controlled via everyday smartphones. At its core, SmartControllerJS establishes a connection between two webpages, one page running on a desktop browser and the other on the user's smartphone. The smartphone webpage loads a controller interface allowing users to control a web application running on their computer's browser. The SmartControllerJS framework enables fast iteration loops when designing interactive user experiments because it has minimal friction and allows for scaling, while having no running costs. We first describe how this library is built, how it can be used, and provide interactive examples. We then present two games designed for public screens along with results from user studies evaluating acceptability and ease of use. Finally, we implement a custom controller based on user feedback and introduce connection monitoring tools. We believe SmartControllerJS can accelerate the design of interactive experiments for researchers in Human-Computer Interaction, and be a useful tool for educational projects. All our code is available at https://github.com/SmartControllerJS and links to all demos can be found in Table I. To explore our demos, we recommend reading this work on a desktop computer with your smartphone in hand.
Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation
Venue: arXiv cs.RO 2022
Year: 2022

Humans have an extraordinary ability to communicate and read the properties of objects by simply watching them being carried by someone else. This level of communicative skills and interpretation, available to humans, is essential for collaborative robots if they are to interact naturally and effectively. For example, suppose a robot is handing over a fragile object. In that case, the human who receives it should be informed of its fragility in advance, through an immediate and implicit message, i.e., by the direct modulation of the robot's action. This work investigates the perception of object manipulations performed with a communicative intent by two robots with different embodiments (an iCub humanoid robot and a Baxter robot). We designed the robots' movements to communicate carefulness or not during the transportation of objects. We found that not only this feature is correctly perceived by human observers, but it can elicit as well a form of motor adaptation in subsequent human object manipulations. In addition, we get an insight into which motion features may induce to manipulate an object more or less carefully.
A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval
Venue: arXiv cs.CV 2022
Year: 2022

Every hour, huge amounts of visual contents are posted on social media and user-generated content platforms. To find relevant videos by means of a natural language query, text-video retrieval methods have received increased attention over the past few years. Data augmentation techniques were introduced to increase the performance on unseen test examples by creating new training samples with the application of semantics-preserving techniques, such as color space or geometric transformations on images. Yet, these techniques are usually applied on raw data, leading to more resource-demanding solutions and also requiring the shareability of the raw data, which may not always be true, e.g. copyright issues with clips from movies or TV series. To address this shortcoming, we propose a multimodal data augmentation technique which works in the feature space and creates new videos and captions by mixing semantically similar samples. We experiment our solution on a large scale public dataset, EPIC-Kitchens-100, and achieve considerable improvements over a baseline method, improved state-of-the-art performance, while at the same time performing multiple ablation studies. We release code and pretrained models on Github at https://github.com/aranciokov/FSMMDA_VideoRetrieval.
Layered Binary Templating: Efficient Detection of Compiler- and Linker-introduced Leakage
Venue: arXiv cs.CR 2022
Year: 2022

Cache template attacks demonstrated automated leakage of user input in shared libraries. However, for large binaries, the runtime is prohibitively high. Other automated approaches focused on cryptographic implementations and media software but are not directly applicable to user input. Hence, discovering and eliminating all user input side-channel leakage on a cache-line granularity within huge code bases are impractical. In this paper, we present a new generic cache template attack technique, LBTA, layered binary templating attacks. LBTA uses multiple coarser-grained side channel layers as an extension to cache-line granularity templating to speed up the runtime of cache templating attacks. We describe LBTA with a variable number of layers with concrete side channels of different granularity, ranging from 64 B to 2MB in practice and in theory beyond. In particular the software-level page cache side channel in combination with the hardware-level L3 cache side channel, already reduces the templating runtime by three orders of magnitude. We apply LBTAs to different software projects and thereby discover data deduplication and dead-stripping during compilation and linking as novel security issues. We show that these mechanisms introduce large spatial distances in binaries for data accessed during a keystroke, enabling reliable leakage of keystrokes. Using LBTA on Chromium-based applications, we can build a full unprivileged cache-based keylogger. Our findings show that all user input to Chromium-based apps is affected and we demonstrate this on a selection of popular apps including Signal, Threema, Discord, and password manager apps like passky. As this is not a flaw of individual apps but the framework, we conclude that all apps that use the framework will also be affected, i.e., hundreds of apps.
Noise tolerance of learning to rank under class-conditional label noise
Venue: arXiv cs.IR 2022
Year: 2022

Often, the data used to train ranking models is subject to label noise. For example, in web-search, labels created from clickstream data are noisy due to issues such as insufficient information in item descriptions on the SERP, query reformulation by the user, and erratic or unexpected user behavior. In practice, it is difficult to handle label noise without making strong assumptions about the label generation process. As a result, practitioners typically train their learning-to-rank (LtR) models directly on this noisy data without additional consideration of the label noise. Surprisingly, we often see strong performance from LtR models trained in this way. In this work, we describe a class of noise-tolerant LtR losses for which empirical risk minimization is a consistent procedure, even in the context of class-conditional label noise. We also develop noise-tolerant analogs of commonly used loss functions. The practical implications of our theoretical findings are further supported by experimental results.
SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation
Venue: arXiv cs.CV 2022
Year: 2022

This paper presents an efficient symmetry-agnostic and correspondence-free framework, referred to as SC6D, for 6D object pose estimation from a single monocular RGB image. SC6D requires neither the 3D CAD model of the object nor any prior knowledge of the symmetries. The pose estimation is decomposed into three sub-tasks: a) object 3D rotation representation learning and matching; b) estimation of the 2D location of the object center; and c) scale-invariant distance estimation (the translation along the z-axis) via classification. SC6D is evaluated on three benchmark datasets, T-LESS, YCB-V, and ITODD, and results in state-of-the-art performance on the T-LESS dataset. Moreover, SC6D is computationally much more efficient than the previous state-of-the-art method SurfEmb. The implementation and pre-trained models are publicly available at https://github.com/dingdingcai/SC6D-pose.
Multimodal sensor fusion in the latent representation space
Venue: arXiv cs.AI 2022
Year: 2022

A new method for multimodal sensor fusion is introduced. The technique relies on a two-stage process. In the first stage, a multimodal generative model is constructed from unlabelled training data. In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion tasks. The method also handles cases where observations are accessed only via subsampling i.e. compressed sensing. We demonstrate the effectiveness and excellent performance on a range of multimodal fusion experiments such as multisensory classification, denoising, and recovery from subsampled observations.
The Case for Non-Volatile RAM in Cloud HPCaaS
Venue: arXiv cs.DC 2022
Year: 2022

HPC as a service (HPCaaS) is a new way to expose HPC resources via cloud services. However, continued effort to port large-scale tightly coupled applications with high interprocessor communication to multiple (and many) nodes synchronously, as in on-premise supercomputers, is still far from satisfactory due to network latencies. As a consequence, in said cases, HPCaaS is recommended to be used with one or few instances. In this paper we take the claim that new piece of memory hardware, namely Non-Volatile RAM (NVRAM), can allow such computations to scale up to an order of magnitude with marginalized penalty in comparison to RAM. Moreover, we suggest that the introduction of NVRAM to HPCaaS can be cost-effective to the users and the suppliers in numerous forms.
Adversarial Attacks on ASR Systems: An Overview
Venue: arXiv cs.SD 2022
Year: 2022

With the development of hardware and algorithms, ASR(Automatic Speech Recognition) systems evolve a lot. As The models get simpler, the difficulty of development and deployment become easier, ASR systems are getting closer to our life. On the one hand, we often use APPs or APIs of ASR to generate subtitles and record meetings. On the other hand, smart speaker and self-driving car rely on ASR systems to control AIoT devices. In past few years, there are a lot of works on adversarial examples attacks against ASR systems. By adding a small perturbation to the waveforms, the recognition results make a big difference. In this paper, we describe the development of ASR system, different assumptions of attacks, and how to evaluate these attacks. Next, we introduce the current works on adversarial examples attacks from two attack assumptions: white-box attack and black-box attack. Different from other surveys, we pay more attention to which layer they perturb waveforms in ASR system, the relationship between these attacks, and their implementation methods. We focus on the effect of their works.
On the homology language of HDA models of transition systems
Venue: arXiv cs.FL 2022
Year: 2022

Given a transition system with an independence relation on the alphabet of labels, one can associate with it a usually very large symmetric higher-dimensional automaton. The purpose of this paper is to show that by choosing an acyclic relation whose symmetric closure is the given independence relation, it is possible to construct a much smaller nonsymmetric HDA with the same homology language.
Empirical Characteristics of Affordable Care Act Risk Transfer Payments
Venue: arXiv cs.CY 2022
Year: 2022

Under the Affordable Care Act (ACA), insurers cannot engage in medical underwriting and thus face perverse incentives to engage in risk selection and discourage low-value patients from enrolling in their plans. One ACA program intended to reduce the effects of risk selection is risk adjustment. Under a risk adjustment program, insurers with less healthy enrollees receive risk transfer payments from insurers with healthier enrollees. Our goal is to understand the elements driving risk transfers. First, the distribution of risk transfers should be based on random health shocks, which are unpredictable events that negatively affect health status. Second, risk transfers could be influenced by factors unique to each insurer, such as certain plans attracting certain patients, the extent to which carriers engage in risk selection, and the degree of upcoding. We create a publicly available dataset using Centers for Medicare and Medicaid Services data that includes insurer risk transfer payments, costs, and premiums for the 2014-2017 benefit years. Using this dataset, we find that the empirical distribution of risk transfer payments is not consistent with the lack of risk selection as measured by the ACA risk transfer formula. Over all states included in our dataset, at least 60% of the volume of transfers cannot be accounted for by a purely normal model. Because we find that it is very unlikely that risk transfer payments are caused solely by random shocks that reflect health events of the population, our work raises important questions about the causes of heterogeneity in risk transfers.
Pattern Spotting and Image Retrieval in Historical Documents using Deep Hashing
Venue: arXiv cs.CV 2022
Year: 2022

This paper presents a deep learning approach for image retrieval and pattern spotting in digital collections of historical documents. First, a region proposal algorithm detects object candidates in the document page images. Next, deep learning models are used for feature extraction, considering two distinct variants, which provide either real-valued or binary code representations. Finally, candidate images are ranked by computing the feature similarity with a given input query. A robust experimental protocol evaluates the proposed approach considering each representation scheme (real-valued and binary code) on the DocExplore image database. The experimental results show that the proposed deep models compare favorably to the state-of-the-art image retrieval approaches for images of historical documents, outperforming other deep models by 2.56 percentage points using the same techniques for pattern spotting. Besides, the proposed approach also reduces the search time by up to 200x and the storage cost up to 6,000x when compared to related works based on real-valued representations.